Looking for breakthrough ideas for innovation challenges? Try Patsnap Eureka!

Multi-source time sequence missing data recovery method based on matrix decomposition

A technology of time series and matrix decomposition, applied in the field of data processing, can solve the problems of failing to make full use of the internal prior information of multi-source time series data, and the quality of missing data recovery is not high, so as to achieve the effect of improving accuracy

Active Publication Date: 2020-11-03
NAVAL AVIATION UNIV
View PDF9 Cites 5 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0004] The purpose of the present invention is to design a multi-source time series missing data recovery method, aiming to solve the problem that the existing technology fails to make full use of the prior information inside the multi-source time series data and the quality of missing data recovery is not high

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • Multi-source time sequence missing data recovery method based on matrix decomposition
  • Multi-source time sequence missing data recovery method based on matrix decomposition
  • Multi-source time sequence missing data recovery method based on matrix decomposition

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0018] The present invention will be described in more detail below in conjunction with the embodiments and the accompanying drawings.

[0019] Such as figure 1 As shown, the embodiment of the present invention discloses a multi-source time series missing data recovery method based on matrix decomposition, and the specific steps are as follows:

[0020] S1, according to the smoothness of the time series, the second-order difference regularization term of the hidden factors of the time series is constructed.

[0021] Consider the row vector of the multi-source time series matrix X as the time series of a certain sensor, and calculate x ij The difference between the two adjacent positions before and after is normalized

[0022]

[0023] Where|x i,j+1 +x i,j-1 -2x ij | represents the second order difference, Indicates the maximum difference of the second order difference in the time series, if Then the time series is considered stable, where C(r≤b) represents the numb...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

PUM

No PUM Login to View More

Abstract

The invention discloses a multi-source time sequence missing data recovery method based on matrix decomposition, and aims at the multi-source time sequence missing data recovery problem, the method considers the data characteristics of a multi-source time sequence under two angles of time and a sensor on the basis of multi-source time sequence matrix decomposition, and the prior information is fully mined. For the time sequence, constraining is carried out by utilizing second-order difference regularization; for multi-sensor data, a basic principle of a graph theory is introduced, and a correlation measurement method of double Pearson coefficients is adopted to obtain a Laplace matrix representing a data relationship of each sensor. Finally, graph Laplace regularization and second-order difference regularization are fused into a matrix decomposition framework, and optimization of a target function is achieved by utilizing a gradient descent method. According to the missing data recovery method provided by the invention, data priori is fully utilized, two regularization constraint conditions are fused, and the missing data recovery method is still effective under the condition of relatively high missing rate.

Description

technical field [0001] The present invention relates to missing data recovery technology. Specifically, the present invention proposes a multi-source time series missing data recovery method based on matrix decomposition, which belongs to the field of data processing technology. Background technique [0002] In a real-world scenario, multiple sensors are deployed in a certain monitoring area to continuously sense the same object and obtain rich information to support different types of sensing applications. These data collected from multi-sensor networks are often referred to as multi-source time series. For example, multiple sensors on offshore buoys monitor marine environmental data (temperature, humidity, pressure, wind speed, wind direction, etc.) to obtain the overall situational awareness of the evaporation waveguide; personal medical systems use multiple sensors to monitor blood pressure, pulse, ECG and other data through wearable devices Know the overall health stat...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

Application Information

Patent Timeline
no application Login to View More
Patent Type & Authority Applications(China)
IPC IPC(8): G06F17/16G06F17/18
CPCG06F17/16G06F17/18Y02A90/10
Inventor 芮国胜刘歌田文飚
Owner NAVAL AVIATION UNIV
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Patsnap Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Patsnap Eureka Blog
Learn More
PatSnap group products

Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic, Popular Technical Reports.

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap|About US| Contact US: help@patsnap.com